Asynchronous dissipative stabilization for stochastic Markov-switching neural networks with completely- and incompletely-known transition rates

Tai, Weipeng; Li, Xinling; Zhou, Jianping; Arik, Sabri

doi:10.1016/j.neunet.2023.01.039

Cited by 21 publications

(3 citation statements)

References 40 publications

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“…The specific derivation process is identical to the proof of inequality (36) ensuring inequality (10) in Theorem 1 and we omitted here. Remark 4 Theorem 2 further proposes a modemismatched filter design approach to ensure that FES (8) is MSFTB and has the FTHP index γ. This design approach allows the switching modes of the filter to differ from those of the system while depending on it through conditional probabilities.…”

Section: Mode-mismatched Filter Designmentioning

confidence: 99%

“…Costa and do Val [4] further studied some fundamental issues of MJSs, including observability and measurability. In recent years, more topics related to MJSs, ranging from almost sure stability, [5] state estimation, [6] fault detection, [7] and dissipativity-based stabilization, [8] to sampled-data synchronization, [9] have been extensively investigated in the automation community.…”

Section: Introductionmentioning

confidence: 99%

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Finite-time ℋ_∞ filtering for Markov jump systems with uniform quantization

Dong 董,

Ma 马,

Zhang 张

et al. 2023

Chinese Phys. B

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This paper is concerned with finite-time H ∞ filtering for Markov jump systems with uniform quantization. The objective is to design quantized mode-dependent filters to ensure that the filtering error system is not only mean-square finite-time bounded but also has a prescribed finite-time H ∞ performance. First, the case where the switching modes of the filter align with those of the MJS is considered. A numerically tractable filter design approach is proposed utilizing a mode-dependent Lyapunov function, Schur’s complement, and Dynkin’s formula. Then, the study is extended to a scenario where the switching modes of the filter can differ from those of the MJS. To address this situation, a mode-mismatched filter design approach is developed by leveraging a hidden Markov model to describe the asynchronous mode switching and the double expectation formula. Finally, a spring system model subject to a Markov chain is employed to validate the effectiveness of the quantized filter design approaches.

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Section: Mode-mismatched Filter Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Finite-time ℋ_∞ filtering for Markov jump systems with uniform quantization

Dong 董,

Ma 马,

Zhang 张

et al. 2023

Chinese Phys. B

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“…Unlike the usual Markovian process where the dwell time follows an exponential distribution and transition rates are constant, the semi-Markovian process allows for non-exponential dwell time and time-variant transition rates [27]. It is also worth mentioning that the CDN model under consideration includes those investigated in [28][29][30] as special cases.…”

mentioning

confidence: 99%

Mean-square asymptotic synchronization of complex dynamical networks subject to communication delay and switching topology

Wang,

Qin,

et al. 2023

Phys. Scr.

Self Cite

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This paper addresses the issue of mean-square asymptotic synchronization (MSAS) of complex dynamical networks with communication delay and switching topology. The communication delay is assumed to be time-variant and bounded, and the switching topology is governed by a semi-Markovian process and allowed to be asymmetric. A distributed control law based on state feedback is presented. Two criteria for the MSAS are derived using a mode-dependent Lyapunov-Krasovskii functional, the Bessel-Legendre integral inequality, and a parameter-dependent convex combination inequality, for the asymmetric and symmetric topology cases, respectively. The scenario of fixed topology is also considered, for which two asymptotic synchronization criteria are proposed. Two simulation examples are provided to illustrate the effectiveness and reduced conservatism of the proposed theoretical results.

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Stability analysis of switched stochastic time‐varying delay neural networks with asynchronous switching aperiodic intermittent control

Fan,

Zhu,

Karimi

2024

Intl J Robust & Nonlinear

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This paper is dedicated to studying the mean square exponential stability of switched stochastic time‐varying delay neural networks with asynchronous switching aperiodic intermittent control. By utilizing the Halanay‐type inequality and designing appropriate asynchronous switching aperiodic intermittent control, several novel stability criteria of switched stochastic delay neural networks with asynchronous aperiodic intermittent control are presented under the Lyapunov function method and Lyapunov functional method. Among them, asynchronous switching aperiodic intermittent controllers are more practical and flexible compared to state feedback controllers. Finally, we provide two examples to verify the effectiveness of the theoretical results.

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Asynchronous dissipative stabilization for stochastic Markov-switching neural networks with completely- and incompletely-known transition rates

Cited by 21 publications

References 40 publications

Finite-time ℋ_∞ filtering for Markov jump systems with uniform quantization

Finite-time ℋ_∞ filtering for Markov jump systems with uniform quantization

Mean-square asymptotic synchronization of complex dynamical networks subject to communication delay and switching topology

Stability analysis of switched stochastic time‐varying delay neural networks with asynchronous switching aperiodic intermittent control

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Asynchronous dissipative stabilization for stochastic Markov-switching neural networks with completely- and incompletely-known transition rates

Cited by 21 publications

References 40 publications

Finite-time ℋ∞ filtering for Markov jump systems with uniform quantization

Finite-time ℋ∞ filtering for Markov jump systems with uniform quantization

Mean-square asymptotic synchronization of complex dynamical networks subject to communication delay and switching topology

Stability analysis of switched stochastic time‐varying delay neural networks with asynchronous switching aperiodic intermittent control

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Finite-time ℋ_∞ filtering for Markov jump systems with uniform quantization

Finite-time ℋ_∞ filtering for Markov jump systems with uniform quantization